Emergency department use following incentives to provide after-hours primary care: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Access to primary care outside of regular working hours is limited in many countries. This study investigates the relation between the after-hours premium, an incentive for primary care physicians to provide services after hours, and less-urgent visits to the emergency department in Ontario, Canada. METHODS: We analyzed a retrospective cohort of a random sample of Ontario residents from April 2002 to March 2006, and a subcohort of patients followed from April 2005 to March 2016. We linked patient and primary care physician data with emergency department visit data. We used fixed-effects regression models to analyze the association between the introduction of the after-hours premium, as well as subsequent increases in the value of the premium, and the number of monthly emergency department visits. RESULTS: The sample consisted of 586 534 patients between 2002 and 2006, and 201 594 patients from 2005 to 2016. After controlling for patient and physician characteristics, seasonality and time-invariant patient confounding factors, introduction of the after-hours premium was associated with a reduction of 1.26 less-urgent visits to the emergency department per 1000 patients per month (95% confidence interval -1.48 to -1.04). Most of this reduction was observed in after-hours visits. Sensitivity analysis showed that the monthly reduction in less-urgent visits to the emergency department was in the range of -1.24 to -1.16 per 1000 patients. Subsequent increases in the after-hours premium were associated with a small reduction in less-urgent visits to the emergency department. INTERPRETATION: Ontario's experience suggests that incentivizing physicians to improve access to after-hours primary care reduces some less-urgent visits to the emergency department. Other jurisdictions may consider incentives to limit less-urgent visits to the emergency department.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".